---
license: mit
datasets:
- inesc-id/FalAR
- inesc-id/camoes_asr
language:
- pt
base_model:
- amalia-llm/AMALIA-9B-1225-SFT
- facebook/seamless-m4t-v2-large
pipeline_tag: automatic-speech-recognition
---
# Model card

AMALIA-FALA-ABERTO is a Portuguese ASR checkpoint based on the [SLAM-ASR architecture](https://arxiv.org/abs/2402.08846). It connects a pre-trained speech encoder to a large language model through a learned projection module, allowing the LLM to generate text transcriptions from speech representations.

## Architecture

The model follows the SLAM-ASR design:

```text
audio → speech encoder → projector → LLM → transcription
```

The speech encoder extracts acoustic representations from the input audio. These representations are mapped by a trainable projector into the embedding space expected by the language model. The LLM then autoregressively generates the transcription.

## Components

* **Base architecture:** SLAM-ASR
* **Speech encoder:** `Seamless`, also available on Hugging Face
* **Language model:** `AMALIA-9B-1225-SFT`
* **Task:** Automatic Speech Recognition
* **Language:** Portuguese, with focus on European Portuguese

## Intended Use

This model is intended for research and experimentation on European Portuguese ASR, especially in settings where speech representations are connected to an LLM instead of using a standalone encoder-decoder ASR model.

Example use cases include:

* transcribing Portuguese speech;
* evaluating LLM-augmented ASR systems;
* comparing SLAM-style ASR with conventional ASR models;
* research on European Portuguese speech processing.

## AMALIA-FALA Usage

AMALIA-FALA models are split into two parts:

1. **Model repository**: contains the model weights and example assets.
2. **Loader repository**: contains the lightweight Python code used to load and run the model.

The model weights are **not cloned manually** by the user. They are downloaded automatically from Hugging Face the first time the model is loaded.

---

### 1. Install the loader package

Install the lightweight AMALIA-FALA loader directly from Hugging Face:

```bash
pip install git+https://huggingface.co/amalia-llm/amalia-fala-loader
```

---

### 2. Run inference on your own audio file

```python
from amalia_fala import pipeline

asr = pipeline(
    "amalia-llm/AMALIA-FALA-ABERTO",
    device="cuda:0",
)

result = asr("/path/to/audio.wav")
print(result["text"])
```

The checkpoint is downloaded automatically from:

```text
amalia-llm/AMALIA-FALA-ABERTO
```

and cached locally by Hugging Face. Future runs reuse the cached checkpoint.

---

### 3. Load the model directly

If you need access to the underlying model and tokenizer:

```python
from amalia_fala import load_model

model, tokenizer, model_config, dataset_config = load_model(
    "amalia-llm/AMALIA-FALA-ABERTO",
    device="cuda:0",
)
```

---

### Notes

- The loader package contains only the Python loading code.
- The model repositories contain the actual checkpoints.
- The checkpoint is downloaded automatically on first use.
- The Hugging Face cache is reused after the first download.
- A GPU is recommended for practical inference speed.

## Training Data

The model was developed in the context of European Portuguese ASR experiments using [FalAR](https://arxiv.org/abs/2605.27062) and [CAMÕES](https://arxiv.org/abs/2508.19721) ASR data. 
FalAR contains more than 5000 hours of parliamentary speech from the *Assambleia da República*. 
CAMÕES  consists of a curated collection of up to 14 sub-corpora containing different domains and speech styles, totaling approximately 425 hours of speech. 
The encoder `AMALIA-speech-encoder` was trained separately and reused as the speech encoder in this SLAM-ASR setup.

## Limitations

This checkpoint is intended as a research artifact. Performance may vary depending on audio quality, speaker domain, recording conditions, and transcription style. The model may be less reliable on noisy audio, long-form speech, code-switching, or domains that differ from the training data.

## Citation / Acknowledgements

This model builds on the SLAM-ASR idea of connecting a speech encoder to a large language model for automatic speech recognition.